A novel AI model for detecting periapical lesion on CBCT: CBCT-SAM.

Objectives: Periapical lesions are not always evident on radiographic scans. Sometimes, asymptomatic or initial periapical lesions on cone-beam computed tomography (CBCT) could be missed by inexperienced dentists, especially when the scan has a large field of view and is not for endodontic treatment purposes. Previously, numerous algorithms have been introduced to assist radiographic assessment and diagnosis in the field of endodontics.

MGIML: Cancer Grading With Incomplete Radiology-Pathology Data via Memory Learning and Gradient Homogenization.

Taking advantage of multi-modal radiology-pathology data with complementary clinical information for cancer grading is helpful for doctors to improve diagnosis efficiency and accuracy. However, radiology and pathology data have distinct acquisition difficulties and costs, which leads to incomplete-modality data being common in applications. In this work, we propose a Memory- and Gradient-guided Incomplete Modal-modal Learning (MGIML) framework for cancer grading with incomplete radiology-pathology data.

Comprehensive learning and adaptive teaching: Distilling multi-modal knowledge for pathological glioma grading.

The fusion of multi-modal data, e.g., pathology slides and genomic profiles, can provide complementary information and benefit glioma grading.

FedOSS: Federated Open Set Recognition via Inter-Client Discrepancy and Collaboration.

Open set recognition (OSR) aims to accurately classify known diseases and recognize unseen diseases as the unknown class in medical scenarios. However, in existing OSR approaches, gathering data from distributed sites to construct large-scale centralized training datasets usually leads to high privacy and security risk, which could be alleviated elegantly via the popular cross-site training paradigm, federated learning (FL). To this end, we represent the first effort to formulate federated open set recognition (FedOSR), and meanwhile propose a novel Federated Open Set Synthesis (FedOSS) framework to address the core challenge of FedOSR: the unavailability of unknown samples for all anticipated clients during the training phase.

FedDM: Federated Weakly Supervised Segmentation via Annotation Calibration and Gradient De-Conflicting.

Weakly supervised segmentation (WSS) aims to exploit weak forms of annotations to achieve the segmentation training, thereby reducing the burden on annotation. However, existing methods rely on large-scale centralized datasets, which are difficult to construct due to privacy concerns on medical data. Federated learning (FL) provides a cross-site training paradigm and shows great potential to address this problem.

Personalized Retrogress-Resilient Federated Learning Toward Imbalanced Medical Data.

Clinically oriented deep learning algorithms, combined with large-scale medical datasets, have significantly promoted computer-aided diagnosis. To address increasing ethical and privacy issues, Federated Learning (FL) adopts a distributed paradigm to collaboratively train models, rather than collecting samples from multiple institutions for centralized training. Despite intensive research on FL, two major challenges are still existing when applying FL in the real-world medical scenarios, including the performance degradation (i.

Instance importance-Aware graph convolutional network for 3D medical diagnosis.

Automatic diagnosis of 3D medical data is a significant goal of intelligent healthcare. By exploiting the abundant pathological information of 3D data, human experts and algorithms can provide accurate predictions for patients. Considering the high cost of collecting exhaustive annotations for 3D data, a sustainable alternative is to develop diagnosis algorithms with merely patient-level labels.

DSI-Net: Deep Synergistic Interaction Network for Joint Classification and Segmentation With Endoscope Images.

Automatic classification and segmentation of wireless capsule endoscope (WCE) images are two clinically significant and relevant tasks in a computer-aided diagnosis system for gastrointestinal diseases. Most of existing approaches, however, considered these two tasks individually and ignored their complementary information, leading to limited performance. To overcome this bottleneck, we propose a deep synergistic interaction network (DSI-Net) for joint classification and segmentation with WCE images, which mainly consists of the classification branch (C-Branch), the coarse segmentation (CS-Branch) and the fine segmentation branches (FS-Branch).

Mutual-Prototype Adaptation for Cross-Domain Polyp Segmentation.

Accurate segmentation of the polyps from colonoscopy images provides useful information for the diagnosis and treatment of colorectal cancer. Despite deep learning methods advance automatic polyp segmentation, their performance often degrades when applied to new data acquired from different scanners or sequences (target domain). As manual annotation is tedious and labor-intensive for new target domain, leveraging knowledge learned from the labeled source domain to promote the performance in the unlabeled target domain is highly demanded.

Branched convolutional neural networks incorporated with Jacobian deep regression for facial landmark detection.

Facial landmark detection is to localize multiple facial key-points for a given facial image. While many methods have achieved remarkable performance in recent years, the accuracy remains unsatisfactory due to some uncontrolled conditions such as occlusion, head pose variations and illumination, under which, the L2 loss function is conventionally dominated by errors from those facial components on which the landmarks are hard predicted. In this paper, a novel branched convolutional neural network incorporated with Jacobian deep regression framework, hereafter referred to as BCNN-JDR, is proposed to solve the facial landmark detection problem.